Responsible AI / Human oversight
Responsible AI grading keeps educators accountable
PaperGrader is designed to assist authorised educators. AI can help prepare a rubric-based draft; it should not independently publish an academic result. The institution controls the rubric, review policy, approval rights and final decision.
For assessment leaders who need controls as well as capability.
Schools, colleges and universities can use this approach when they need to evaluate how AI-assisted grading fits their academic policy, student communication, quality assurance and review obligations.
Automation without an accountable decision path is not responsible assessment.
A score can be affected by source-document quality, question mapping, rubric interpretation and the limits of AI assistance. Responsible use means making uncertainty visible, retaining the evidence used in review and assigning final approval to an authorised human.
How the evaluation workflow operates
01
Approve the rubric
The institution sets the assessment criteria and controls who may change them.
02
Keep uncertainty visible
Low-confidence, incomplete or ambiguous material should remain identifiable for human review.
03
Review AI-assisted drafts
Educators inspect the original answer, rubric context and draft suggestion before deciding the result.
04
Record interventions
Score changes, approval notes and relevant reviewer actions form part of the decision record.
05
Reassess the workflow
A material change to models, rubrics, subjects or scan processes should trigger renewed evaluation before wider use.
The institution remains the decision-maker.
PaperGrader does not replace institutional responsibility for final marks, moderation, appeals, fairness review or student communication. The appropriate review process depends on the stakes of the assessment and should be set by the institution.
Operational benefits
- Human sign-off rather than unattended grade publication
- Institution-controlled rubrics and reviewer roles
- Visible review of low-confidence or ambiguous work
- Traceable reviewer overrides and approvals
- A basis for deployment-specific governance and evaluation